PP01 International pooling project of mammographic density - insights of a marker of breast cancer risk from 22 diverse countries
Bibliographic record
Abstract
Background Mammographic density (MD) is a strong intermediate risk factor for breast cancer (BC) and, having both genetic and environmental determinants, may account for the over 6-fold international variations in BC incidence rates. The International Pooling Project of Mammographic Density is a worldwide collaborative project of MD targeting populations spanning the BC incidence range. The aims of the project are to (i) describe international variations in overall and age-specific distributions of MD, (ii) assess whether international variations in MD are explained by variations in the distributions of individual-level determinants of this marker and (iii) examine whether international variations in MD correlate with corresponding BC incidence rates. Methods Each contributing study provided comparable data on MD risk factors and mammographic images from a random sample of ˜ 400 general population women, who had undergone screening mammography. Images were transferred in digitised screen-film, full-field or computed radiography digital DICOM format (raw or processed). Images were randomly allocated into batches for MD reading using the Cumulus 6 thresholding software, by experienced readers who were blinded to study and individual-level factors. Data on MD determinants were pooled and linked with MD readings. Results are calibrated according to type of digital image (raw to processed), and adjusted for image type. Results 22 countries and approximately 12,000 women are included, spanning populations with age-standardised BC incidence rates (ASR) of 25.8 (India) to 99 (The Netherlands) per 100,000 woman-years. To date, for 9,635 participants data have been pooled (results will be updated). The MD risk factors vary greatly across populations, for example: mean age at menarche (in years) was 14.3 (95% CI 14.1–14.5) in Korean women and 12.6 (12.5–12.8) in Mexican women; mean parity was 3.8 (3.6–4.1) in Egyptian women and 1.3 (1.1–1.4) in those from Hong Kong; and mean BMI (in kg/m2) was 33.7 (33.1–34.3) in Egyptian women compared to 22.3 (21.6–22.9) in those from India. Differences in MD according to these distributions will be presented. Discussion The international perspective of this study generated large exposure heterogeneity enabling a wider investigation of MD determinants and the extent to which MD is an intermediate marker of BC risk, both within and between populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".